Rare MATLAB Source Code for Reinforcement Learning with Q-Learning Implementation
Uncommon MATLAB source code for reinforcement learning featuring detailed Q-learning programming process with algorithm implementation and function explanations.
Robot Path Finding Using Reinforcement Learning
Robot path finding based on reinforcement learning algorithms with grid map environment implementation
Inverted Pendulum as a Classic Example
Among various nonlinear systems, the inverted pendulum serves as a classic example where reinforcement learning is implemented for control.
Original Reinforcement Learning Code: Foundation for Enhanced Algorithm Implementation
Original reinforcement learning code serves as a fundamental starting point that can be significantly improved through comprehensive documentation, algorithm optimization, and modular function implementations
Research Papers on Reinforcement Learning
A collection of valuable reinforcement learning papers featuring practical examples, detailed algorithm workflows, and implementation approaches to enhance understanding and application
Q-Learning Algorithm for Reinforcement Learning with MATLAB Implementation
MATLAB code implementation of the Q-learning algorithm for optimal path finding. This comprehensive resource provides intuitive understanding through practical coding examples, featuring state-action value updates and epsilon-greedy exploration strategies.
Reinforcement Learning Q-Learning Algorithm: Theory and Implementation
The Q-learning algorithm in reinforcement learning continuously reinforces specific actions through value iteration. This enhanced description includes practical implementation details, key algorithmic components, and function explanations for developers working with Q-learning.
Neural Control (Reinforcement Learning) for Tanker Heading
Neural Control (Reinforcement Learning) for Tanker Heading - Implementation using neural networks and reinforcement learning algorithms for autonomous navigation.
MATLAB Implementation of Q-Learning Algorithm
A MATLAB program for Q-Learning implementation, featuring value iteration updates, reward matrix processing, and epsilon-greedy policy implementation - highly valuable for reinforcement learning and adaptive dynamic programming research
Implementing Obstacle Avoidance for Mobile Laser Ranging Robots Using Reinforcement Learning
Implementation of obstacle avoidance for mobile laser ranging robots through reinforcement learning algorithms with code-level technical insights